Job Description

Partners with data scientists, data managers, analysts, infrastructure engineers and peer AI Engineers to develop, operationalize, integrate, and scale new algorithmic products.Engages in proof of concepts and experiments to evaluate new models and technology.Develops high quality, standard-compliant and tested code.Refactors code of others as necessary.Contributes code and tools to central re-usable libraries and repositories.Develops data models, model features, data quality tests, ETL/ELT pipelines, and distributed compute architectures.Deploys algorithmic products across the clouds, using cloud-native services.Manages and automates cloud resources (IaC).Implements DevOps, DataOps and MLOps principles in day-to-day work.Develops continuous integration, delivery, and training pipelines. Leverages observability and automation tools to manage operations of algorithmic products across the stack.

Job Qualifications

Demonstrated mastery in applied foundations data technologies such as: SQL, Relational databases, ER models/diagrams, and/or database framework.Familiar with CICD process and related toolsets, such as pytest, Sonarqube, Github Actions, Jenkins etc.Understand algorithms like Collaborative Filtering, XGboost, etc.Have working knowledge in programming languages such as Python, Java, spark.Demonstrated skills or familiar with Big Data technologies (Hadoop, Spark, Hive, Impala) and NoSQL databases.Understand algorithm engineering lifecycle and familiar with software engineering methodology.Working experience on cloud computing. Azure cloud is preferred.Strong written and verbal communication skills to influence others.Demonstrated ability to handle multiple priorities.Ability to work collaboratively across functions.